Representation Of Flow Research Articles

Interface Shapes in Microfluidic Porous Media: Conditions Allowing Steady, Simultaneous Two-Phase Flow

Microfluidic devices offer unique opportunities to directly observe multiphase flow in porous media. However, as a representation of flow in geological pore networks, conventional microfluidics face several challenges. One is whether steady simultaneous two-phase flow through a two-dimensional network is possible without fluctuating occupancy of the pore constrictions. Flow without fluctuations can occur only if the flow paths of the two phases can cross on the 2D network; this requires that wetting phase can form a bridge across the gap between grains at a pore constriction while non-wetting phase flows through the constriction. We consider the conditions under which this is possible as a function of the local capillary pressure and the geometry of the constriction. Using the Surface Evolver software, we determine conditions for stable interfaces in constricted geometries, the range of capillary pressures at which bridging can occur, and those where the wetting phase would re-invade the constriction to block the flow of the non-wetting phase (“snap-off”). If a constriction is long and either straight or uniformly curved, snap-off occurs at the same capillary pressure as bridging. For constrictions of concave shape, which we represent as constrictions between cylindrical grains, however, we find a range of capillary pressures at which bridging is stable; the range is greater the narrower the diameter of the cylinders (i.e. the more strongly concave the throat) relative to the width of the constriction. For smaller-diameter pillars, the phenomenon of “Roof” snap-off as non-wetting phase invades a downstream pore body, is predicted not to occur.

A Review of Numerical Modelling of Morphodynamics in Braided Rivers: Mechanisms, Insights and Challenges

In the past decade, the numerical modelling of braided river morphodynamics has experienced a significant advance due to the increasing computer power and the development of numerical techniques. Numerical models are quite efficient in exploring scenarios with different settings, and they can be applied to investigate the complicated physics laws of natural braided rivers and manage complex river engineering problems. However, braided river models are far from fully developed, e.g., the representation of flow and sediment transport, model sensitivity, essential effects of sediment transport, bank erosion and vegetation, and require intensive refinement and validation to enhance their prediction accuracy. The recent application of advanced field measurement techniques offers model development a new chance by providing abundant measurement data of a high quality. The present study reviews the essential mechanisms and applications of typical braided river models; compares their accuracy; discusses the recent progress, advantages and shortcomings; and illustrates the challenges and future research trends.

The use of multi-zone modelling for tunnel fires

This paper introduces the use of a multi-zone modelling approach for analysing smoke spread in tunnels. The approach suggested in this paper is based on an existing model, i.e., the Multi-Zone Fire model developed for large spaces. The Multi-Zone Fire model has been adapted and modified for tunnel fire scenarios by including features that consider longitudinal ventilation flow, tunnel gradient and tunnel section representations. An evaluation of the model has been conducted through benchmarking against experimental data from the BeNeLux tunnel experiments and the Runehamar tunnel fire experiments. The results from the Multi-Zone Fire model results were also compared against results from the Fire Dynamics Simulator. The results of the benchmarking exercise indicate that the multi-zone approach can be a time-efficient and useful tool for studying tunnel fire dynamics. The Multi-Zone Fire model performs well 50–200 m from the fire for heat release rates of 5–20 MW and moderate longitudinal ventilation flows. The model results are more conservative for the studied scenario with a higher heat release rate.

Optimal grid expansion under future electricity demand for groundwater irrigation in Ethiopia

Ethiopia is a lower-income country with an agrarian based, fast-growing economy that is susceptible to climate change impacts. As the nation continues to develop, domestic electricity consumption and electricity export opportunities will increase alongside a growing electricity demand for groundwater irrigation to mitigate the effects of drought. There are upcoming mega-dams– the Grand Ethiopian Renaissance Dam, Genale Dawa III, and Koysha – that will triple the electricity generation capacity. However, the central electricity grid is not sufficient to efficiently utilize this new capacity. We model future grid expansion scenarios by building a regional-scale linear optimal power flow model based on a transshipment representation of power flow that guides cost-optimal generation operations and transmission expansion, while satisfying competing electricity demand for domestic electricity consumption, groundwater irrigation, and international electricity export.We first evaluate cost-optimal generation and transmission expansion for a baseline case, with an estimate of current Ethiopian electricity demand. We then consider five potential future demand scenarios. These demand scenarios include estimates of both present and future domestic electricity demand, multiple levels of productive electricity demand for groundwater irrigation, and demand for international exports. Compared to the baseline, seasonal capacity factors for generators in the five model scenarios increased when collocated with an increase in electricity demand except for the generators that already operated at maximum capacity. All scenarios with higher domestic regional demand than the baseline surpass the transmission expansion planned by the Ethiopian Electric Power Corporation, independent of changes in irrigation and export demand. Our analysis suggests that the current expansion plan is not sufficient to meet future demand. Our research contributes to the literature by creating an optimal power flow model that can be applied in data-poor environments to model endogenous electricity infrastructure decisions under different scenarios of internal and external demand.

Urban Metabolism Characterization from the National to the Regional Scale: A Case Study of Lisbon

Cities are complex systems with a key role in economic development and in improving the environmental sustainability of nations. Therefore, a comprehensive understanding of urban systems is fundamental to designing relevant policies and initiatives. Urban metabolism characterization may provide a holistic framework for analyzing urban systems in the context of the broader national metabolism. This paper provides a methodology to characterize the metabolism of an urban area, such as the Lisbon metropolitan area, taking as a source of data the socioeconomic metabolism of the country. The national metabolism is established by computing physical input-output tables. The downscaling to the urban level is accomplished with a variety of sector-specific parameters, from which an urban mass balance is established for a set of economic activities. The results obtained for the case study of Portugal and the city of Lisbon suggest that the share of services and the type of manufacturing industries in the economic structure of Lisbon, together with the higher resource productivity in the most representative economic sectors, are key aspects of the metabolism that contribute to Lisbon having higher overall resource productivity. The representation of the metabolism flows revealed key differences between the supply chains of Lisbon and the national flows, as well as a strong dependence of the Lisbon economy on material flows from other regions.

Alternative flow models, vector Green functions and boundary integral flow representations in ship and offshore hydrodynamics

Wave diffraction-radiation by large bodies such as offshore structures and ships advancing in regular waves or in calm water is widely analyzed via panel methods based on a boundary integral flow representation and a Green function that satisfies the relevant free-surface boundary condition. The boundary integral flow representations solved in existing panel methods express the flow potential in terms of distributions of sources and dipoles, related to a Green function and its gradient, over the body surface. Other notable features of existing boundary integral flow representations are that they can be ill-posed for a set of irregular frequencies, and that – for a ship advancing in calm water or in regular waves – they involve a troublesome line integral around the ship waterline. Several alternative boundary integral flow representations that are well suited for wave diffraction-radiation by offshore structures or by ships advancing in regular waves or in calm water are given in this study. These representations, based on an alternative linear flow model (given previously) and an alternative vector Green function (not previously considered), are weakly singular and do not involve a waterline integral. The influence coefficients related to these new flow representations consist of Rankine components associated with complementary types of weakly singular Rankine singularities, and Fourier components defined by a Fourier superposition of elementary waves. Both the Rankine and Fourier components of the influence coefficients can be evaluated simply and efficiently. In particular, the Fourier component can be evaluated via the Fourier–Kochin (FK) method, in which the dominant computational task is independent of the number of panels that approximate the body surface. Thus, the new boundary integral flow representations given in this study and the FK method expounded previously provide a practical new basis for the evaluation of three main classes of wave diffraction-radiation by ships and offshore structures via panel methods.

Marine propeller underwater radiated noise prediction with the FWH acoustic analogy Part 2: Assessment of model scale propeller hydroacoustic performance under non-uniform flow conditions

This study presents the model scale benchmark, The Princess Royal, propeller's hydrodynamic performance, including cavitation extensions and URN operating in a non-uniform wake field. The developed V-AMR technique was used in the numerical calculations to accurately solve the tip vortex flow and better representation the tip vortex cavitation (TVC) in the propeller slipstream. The sheet and tip vortex cavitation was modelled using the Schnerr-Sauer cavitation model. A hybrid method, combining the DES and permeable formulation of the FWH equation, was used for predicting the propeller URN at four different operating conditions corresponding to full-scale operating conditions. The numerical results were first validated with the experimental data obtained in the cavitation tunnel through the propeller hydrodynamic characteristics, cavitation extension and URN in model scale. Then, the propeller URN predictions using a hybrid method were extrapolated to full-scale with the ITTC extrapolation procedure to compare the numerical results with the extrapolated experimental data and full-scale measurements. The results showed that the cavitation extensions on and off the blades were satisfactorily predicted in the numerical calculations compared to the model-scale campaigns and full-scale sea trial observations. However, the same cavitation dynamics and TVC could not be predicted in conditions where the weak and incipient TVC were present between the numerical calculations and model-scale test campaign. Also, the numerical calculations underpredicted the model scale propeller URN at certain frequencies compared to model scale experimental data, except for the highest loading condition. Akin to the comparisons of model scale propeller URN between the numerical calculations and model-scale test data, the extrapolated propeller URN was generally underpredicted at a certain frequency range of the noise spectrum in the numerical calculations compared to the full-scale measurements. This underprediction in the numerical calculations can be associated with the lack of cavitation dynamics, especially TVC, compared to experimental and full-scale observations.

Viscoelastic fluid flow over a horizontal flat plate with various boundary slip conditions and suction effects

This study examines the numerical representation of fluid flow on the Maxwell model in a double-diffusive boundary layer over a horizontal plate.

Self-Supervised Latent Representations of Network Flows and Application to Darknet Traffic Classification

Characterizing network flows is essential for security operators to enhance their awareness about cyber-threats targeting their networks. The automation of network flow characterization with machine learning has received much attention in recent years. To this aim, raw network flows need to be transformed into structured and exploitable data. In this research work, we propose a method to encode raw network flows into robust latent representations exploitable in downstream tasks. First, raw network flows are transformed into graph-structured objects capturing their topological aspects (packet-wise transitional patterns) and features (used protocols, packets’ flags, etc.). Then, using self-supervised learning techniques like Graph Auto-Encoders and Anonymous Walk Embeddings, each network flow graph is encoded into a latent representation that encapsulates both the structure of the graph and the features of its nodes, while minimizing information loss. This results in semantically-rich and robust representation vectors which can be manipulated by machine learning algorithms to perform downstream network-related tasks. To evaluate our network flow embedding models, we use probing flows captured with two /20 network telescopes and labeled using reports originating from different sources. The experimental results show that the proposed network flow embedding method allows for reliable darknet probing activity classification. Furthermore, a comparison between our self-supervised approach and a fully-supervised graph convolutional network shows that, in situations with limited labeled data, the downstream classification model that uses the derived latent representations as inputs outperforms the fully-supervised graph convolutional network. There are many applications of this research work in cybersecurity, such as network flow clustering, attack detection and prediction, malware detection, vulnerability exploit analysis, and inference of attacker’s intentions.

Evaluation of Multiplicative Weight of Covariance Matrix on Hybrid Data Assimilation Schemes

This research develops a comparative study between different multiplicative weights that are assigned to the covariance matrix that represents the background error in two hybrid assimilation schemes: 3DEnVAR and 4DEnVAR. These weights are distributed between the static and time-invariant matrix and the matrix generated from the perturbations of a previous ensemble. The assigned values are 25%, 50%, and 75%, always having as a reference the ensemble matrix. The experiments are applied to the short-range Prediction System (SisPI) that works operationally at the Institute of Meteorology. The impact of Tropical Storm Eta on November 7 and 8, 2020 was selected as a study case. The results suggest that by giving the main weight to the ensemble matrix more realistic solutions are achieved because it shows a better representation of the synoptic flow. On the other hand, it is observed that 3DEnVAR method is more sensitive to multiplicative weight change of the first guess. More realistic results are obtained with 50% and 75% relations with 4DEnVAR method, whereas with 3DEnVAR a weight of 75% for the ensemble matrix is required.

Spectral Representation Learning and Fusion for Autonomous Vehicles Trip Description Exploiting Recurrent Transformer

A thorough analysis and comprehension of the entire cue set in visual data are indispensable for an ideal video description model. As outlined in recent algorithm proposals, video descriptions have primarily been generated by learning RGB and optical flow representations rather than exploring and incorporating the media’s spectral components referring to the patterns or characteristics in the distribution of colors or intensities across different frequencies or wavelengths of light. These components may enhance the description quality and impact the generated text for accuracy, diversity, and coherence. We propose a novel Fourier-based algorithm for extracting spectral features in 3D visual volume by decomposing the video signal into its frequency components, to fill this research gap. Further, the captured spectral features are fused with learned spatial and temporal representations in recurrent transformer architecture for accurate content understanding and appropriate description generation in natural language. The transformer includes an external memory module that produces summarized memory states based on the history of previously observed video fragments and already-generated sentences. These memory states ensure the establishment of sound semantic and linguistic cues. As a result, our proposed algorithm integrates spatial, temporal, spectral, and semantic representations for precise and grammatically accurate descriptions. The effectiveness of our proposed algorithm for the coherent and diverse video description is demonstrated through qualitative and quantitative experimentation on the DeepRide driving trip description dataset. A comprehensive ablation study validates the efficacy of the spectral features fusion with spatial and temporal visual representations for the rich video-to-textual narration generation.

Scale Analysis on Unstructured Grids: Kinetic Energy and Dissipation Power Spectra on Triangular Meshes

AbstractFourier spectra are powerful tools to analyze the scale behavior of turbulent flows. While such spectra are mathematically based on regular periodic data, some state‐of‐the‐art ocean and climate models use unstructured triangular meshes. Observational data is often also available only in an unstructured fashion. In this study, scale analysis specifically for the output of models with triangular meshes is discussed and the representable wavenumbers for Fourier analysis are derived. Aside from using different interpolation methods and oversampling prior to the computation of Fourier spectra, we also consider an alternative scale analysis based on the Walsh–Rademacher basis, that is, indicator functions. It does not require interpolation and can be extended to general unstructured meshes. A third approach based on smoothing filters which focus on grid scales is also discussed. We compare these methods in the context of kinetic energy and dissipation power of a turbulent channel flow simulated with the sea ice‐ocean model FESOM2. One simulation uses a classical viscous closure, another a new backscatter closure. The latter is dissipative on small scales, but anti‐dissipative on large scales leading to more realistic flow representation. All three methods clearly highlight the differences between the simulations as concerns the distribution of dissipation power and kinetic energy over scales. However, the analysis based on Fourier transformation is highly sensitive to the interpolation method in case of dissipation power, potentially leading to inaccurate representations of dissipation at different scales. This highlights the necessity to be cautious when choosing a scale analysis method on unstructured grids.

Deformable Image Registration Using Vision Transformers for Cardiac Motion Estimation from Cine Cardiac MRI Images.

Accurate cardiac motion estimation is a crucial step in assessing the kinematic and contractile properties of the cardiac chambers, thereby directly quantifying the regional cardiac function, which plays an important role in understanding myocardial diseases and planning their treatment. Since the cine cardiac magnetic resonance imaging (MRI) provides dynamic, high-resolution 3D images of the heart that depict cardiac motion throughout the cardiac cycle, cardiac motion can be estimated by finding the optical flow representation between the consecutive 3D volumes from a 4D cine cardiac MRI dataset, thereby formulating it as an image registration problem. Therefore, we propose a hybrid convolutional neural network (CNN) and Vision Transformer (ViT) architecture for deformable image registration of 3D cine cardiac MRI images for consistent cardiac motion estimation. We compare the image registration results of our proposed method with those of the VoxelMorph CNN model and conventional B-spline free form deformation (FFD) non-rigid image registration algorithm. We conduct all our experiments on the open-source Automated Cardiac Diagnosis Challenge (ACDC) dataset. Our experiments show that the deformable image registration results obtained using the proposed method outperform the CNN model and the traditional FFD image registration method.

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

High resolution flow with glazing flow for optimized flow detection in transjugular intrahepatic portosystemic stent shunt (TIPS): First results.

Ultrasound follow-up of transjugular intrahepatic portosystemic shunt (TIPS) is challenging due to the bent course of the stent-graft. Aim of this retrospective study was to assess to which extent the combination of HR flow with Glazing Flow improves hemodynamic assessment in the ultrasound follow-up of TIPS. Comparative studies with CCDS and High Resolution (HR)-Flow with Glazing Flow were evaluated regarding image quality and artifacts on a 5-point scale (0 = cannot be assessed up to 5 = maximum image quality without artifacts). In all cases, an experienced examiner performed the examinations with a 1-6 MHz probe (Resona 7, Mindray). 61 ultrasound examinations in 48 patients were performed; the mean patient age was 54±14.2 years. The use of HR-Flow with Glazing Flow resulted in an improved flow display in 55/61 cases (90.2%). Both methods correlated well (r = 0.71), but HR flow with Glazing flow values were in general higher than CCDS values. The reading resulted in an average value of 2.52±0.54 for CCDS and 3.52±0.57 for HR flow with Glazing flow (p = 0.013). The combination of HR-Flow and Glazing Flow results in improved flow representation and reduction of artifacts in the ultrasound follow-up of TIPS.

Infrastructure planning for ride-hailing services using shared autonomous electric vehicles

Ride-hailing services using shared autonomous electric vehicles (SAEVs) are soon expected to serve a significant portion of the public transportation demands, especially in metropolitan cities. The transportation demands will arise randomly across all transportation nodes and during all hours of operation. This spatio-temporal nature of the transportation demand together with the constraints of trip length, battery size and consumption rate, and power network will guide the actions of the SAEVs. These actions are pair-with-demand, park, charge, and relocate, which in turn will determine the necessary infrastructure plan for ride-hailing services. The plan will comprise SAEV fleet size, charging hub locations across the nodes of the transportation network, and the number of charging stations in each hub. We formulate a profit-maximizing mixed integer linear model comprising a space-time-battery representation of the transportation network and a DC power flow representation of the power network. The model, intended for developing infrastructure plans for ride-hailing services, is robustified to accommodate randomness in travel and electric power demand. The model solution is demonstrated via an illustrative numerical case study representing the Tampa Bay region of Florida, USA. The results show how the infrastructure plans and their profitability depend on the demand fulfillment goal, battery size, battery consumption rate, and power network configurations. A linear relaxation of some of the integer variables of the model to reduce computational time is discussed.

Plastic futures and their CO2 emissions.

Plastics show the strongest production growth of all bulk materials and are already responsible for 4.5% of global greenhouse gas emissions1,2. If no new policies are implemented, we project a doubling of global plastic demand by 2050 and more than a tripling by 2100, with an almost equivalent increase in CO2 emissions. Here we analyse three alternative CO2 emission-mitigation pathways for the global plastics sector until 2100, covering the entire life cycle from production to waste management. Our results show that, through bio-based carbon sequestration in plastic products, a combination of biomass use and landfilling can achieve negative emissions in the long term; however, this involves continued reliance on primary feedstock. A circular economy approach without an additional bioeconomy push reduces resource consumption by 30% and achieves 10% greater emission reductions before 2050 while reducing the potential of negative emissions in the long term. A circular bioeconomy approach combining recycling with higher biomass use could ultimately turn the sector into a net carbon sink, while at the same time phasing out landfilling and reducing resource consumption. Our work improves the representation of material flows and the circular economy in global energy and emission models, and provides insight into long-term dynamics in the plastics sector.

Darcy-Forchheimer flow of Prandtl-Eyring nanofluid subjected to a Riga plate of varying thickness along with Brownian diffusion, thermophoresis and non-uniform heat source/sink effects

This article is subjected to the characteristics of an electro-magnetohydrodynamic (EMHD) boundary layer movement of a nanofluid induced by a Gailitis device with variable thickness. A viscous incompressible Prandtl-Eyring nanofluid saturates the porous medium through Darcy-Forchheimer expression. Heat transfer process involves the influence of non-linear thermal radiation, viscous dissipation, non-uniform heat source/sink, and convective boundary conditions. Moreover, Brownian motion and thermophoresis mechanisms are taken into consideration. The governing mathematical model is transmuted with appropriate similarity transformations into a non-dimensional system of ordinary differential equations (ODEs). Numerical solution for this system is acquired through the bvp4c solver in MATLAB software. Velocity, temperature, and concentration profiles are scrutinized through graphical representation for various flow controlling parameters. The impact of these parameters on local values of skin friction coefficient, Nusselt number, and Sherwood number is analyzed through computational data specified in a tabular form.

Through an Ice Sheet, Darkly

Through an Ice Sheet, Darkly

Spatio-Temporal Image Representation and Deep-Learning-Based Decision Framework for Automated Vehicles

Driving maneuver decision-making is critical to the development and mass deployment of automated vehicles (AVs). The prevailing approaches are stuck with either specific optimization objectives or separate maneuver planning. Inspired by the inherent driving mechanism of human beings, we develop a comprehensive deep learning scheme that extracts and establishes informative image-based representation of dynamic traffic flows and then learns driving strategies from collected driving datasets based on convolutional neural networks (CNNs). Our scheme can effectively capture comprehensive and dynamic features of traffic flows surrounding the ego car and construct dynamic motion images through processing the spatio-temporal signals of all neighbor cars. These constructed virtual images cover all informative dynamic states of the neighbor cars, located in the maneuver-critical area, defined as the motion-sensitive area (MSA). Then, sequential spatio-temporal images together with labeled driving behaviors are fed into our proposed CNN model. The network can extract underlying motion patterns and learn proper driving behaviors, including both the lateral maneuver and longitudinal speed. We demonstrate the effectiveness of the proposed scheme in a typical highway scenario. Results suggest that the proposed scheme merits further investigation to promote the launch of AVs.